Computer Science ›› 2020, Vol. 47 ›› Issue (4): 112-118.doi: 10.11896/jsjkx.190200342

Special Issue: Medical Imaging

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Approach to Classification of Eye Movement Directions Based on EEG Signal

CHENG Shi-wei1, CHEN Yi-jian1, XU Jing-ru1, ZHANG Liu-xin2, WU Jian-feng3, SUN Ling-yun4   

  1. 1 School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China;
    2 Lenovo Research,Beijing 100085,China;
    3 Institute of Industrial Design,Zhejiang University of Technology,Hangzhou 310023,China;
    4 State Key Lab of CAD&CG,Zhejiang University,Hangzhou 310027,China
  • Received:2019-04-26 Online:2020-04-15 Published:2020-04-15
  • Contact: CHENG Shi-wei,born in 1981,Ph.D,professor,Ph.D supervisor,is senior member of China Computer Federation.His main research interests include human-computer interaction.
  • Supported by:
    This work was supported by the National Key Research & Development Program of China (2016YFB1001403) and National Natural Science Foundation of China (61772468,61672451).

Abstract: In order to improve the accuracy of eye movement directions identification based on electro-oculogram (EOG) signals,this paper utilized the electrooculogram (EEG) signals containing EOG artifacts and proposed a new approach to classify eye movement directions.Firstly,EEG signals from the 8 channels in the frontal lobe of the human brain are collected,and EEG data pre-processing is made ,including data normalization and least squares based denoising.Then support vector machine based methodis applied to perform multiple binary-classification,and finally voting strategy is used to solve four-classification problems,thus achieving eye movement directions identification.The experiment results show when using the approach of this paper to classify eye movement directions,the classification accuracy rates in the upper,lower,left and right directions are 78.47%,72.22%,84.03%,79.86% respectively,and the average classification accuracy rates reach 78.65%.In addition,compared with the existed classification methods,the classification accuracy rate of this paper is higher,and the classification algorithm is simpler.It is validated the feasibility and effectiveness of using EEG signals to identify eye movement directions.

Key words: Brain-computer interface, Electroencephalogram, Electro-oculogram, Eye tracking, Human-computer interaction

CLC Number: 

  • TP311
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